


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

a       	=  [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

a       	=  [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

a       	=  []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
							# loop over downblocks/upblocks

							for j in range(2):
														# loop over resnets/attentions for downblocks
														a       	=  F'''down_blocks.{i}.resnets.{j}.'''
														a       	=  F'''input_blocks.{3*i + j + 1}.0.'''
														unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

														if i < 3:
																					# no attention layers in down_blocks.3
																					a       	=  F'''down_blocks.{i}.attentions.{j}.'''
																					a       	=  F'''input_blocks.{3*i + j + 1}.1.'''
																					unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

							for j in range(3):
														# loop over resnets/attentions for upblocks
														a       	=  F'''up_blocks.{i}.resnets.{j}.'''
														a       	=  F'''output_blocks.{3*i + j}.0.'''
														unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

														if i > 0:
																					# no attention layers in up_blocks.0
																					a       	=  F'''up_blocks.{i}.attentions.{j}.'''
																					a       	=  F'''output_blocks.{3*i + j}.1.'''
																					unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

							if i < 3:
														# no downsample in down_blocks.3
														a       	=  F'''down_blocks.{i}.downsamplers.0.conv.'''
														a       	=  F'''input_blocks.{3*(i+1)}.0.op.'''
														unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

														# no upsample in up_blocks.3
														a       	=  F'''up_blocks.{i}.upsamplers.0.'''
														a       	=  F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

a       	=  'mid_block.attentions.0.'
a       	=  'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							a       	=  F'''mid_block.resnets.{j}.'''
							a       	=  F'''middle_block.{2*j}.'''
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					lowercase_					=      {k: k for k in unet_state_dict.keys()}
					for sd_name, hf_name in unet_conversion_map:
										lowercase_					=      sd_name
					for k, v in mapping.items():
										if "resnets" in k:
															for sd_part, hf_part in unet_conversion_map_resnet:
																				lowercase_					=      v.replace(lowercase_    ,				lowercase_	)
															lowercase_					=      v
					for k, v in mapping.items():
										for sd_part, hf_part in unet_conversion_map_layer:
															lowercase_					=      v.replace(lowercase_    ,				lowercase_	)
										lowercase_					=      v
					lowercase_					=      {v: unet_state_dict[k] for k, v in mapping.items()}
					return new_state_dict


# ================#
# VAE Conversion #
# ================#

a       	=  [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
							# down_blocks have two resnets
							for j in range(2):
														a       	=  F'''encoder.down_blocks.{i}.resnets.{j}.'''
														a       	=  F'''encoder.down.{i}.block.{j}.'''
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														a       	=  F'''down_blocks.{i}.downsamplers.0.'''
														a       	=  F'''down.{i}.downsample.'''
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														a       	=  F'''up_blocks.{i}.upsamplers.0.'''
														a       	=  F'''up.{3-i}.upsample.'''
														vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

							# up_blocks have three resnets
							# also, up blocks in hf are numbered in reverse from sd
							for j in range(3):
														a       	=  F'''decoder.up_blocks.{i}.resnets.{j}.'''
														a       	=  F'''decoder.up.{3-i}.block.{j}.'''
														vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
							a       	=  F'''mid_block.resnets.{i}.'''
							a       	=  F'''mid.block_{i+1}.'''
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


a       	=  [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					return w.reshape(*w.shape    ,				1    ,				1	)


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					lowercase_					=      {k: k for k in vae_state_dict.keys()}
					for k, v in mapping.items():
										for sd_part, hf_part in vae_conversion_map:
															lowercase_					=      v.replace(lowercase_    ,				lowercase_	)
										lowercase_					=      v
					for k, v in mapping.items():
										if "attentions" in k:
															for sd_part, hf_part in vae_conversion_map_attn:
																				lowercase_					=      v.replace(lowercase_    ,				lowercase_	)
															lowercase_					=      v
					lowercase_					=      {v: vae_state_dict[k] for k, v in mapping.items()}
					lowercase_					=      ["""q""", """k""", """v""", """proj_out"""]
					for k, v in new_state_dict.items():
										for weight_name in weights_to_convert:
															if F'''mid.attn_1.{weight_name}.weight''' in k:
																				print(F'''Reshaping {k} for SD format'''	)
																				lowercase_					=      reshape_weight_for_sd(lowercase_	)
					return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


a       	=  [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
a       	=  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a       	=  re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
a       	=  {'q': 0, 'k': 1, 'v': 2}


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					lowercase_					=      {}
					lowercase_					=      {}
					lowercase_					=      {}
					for k, v in text_enc_dict.items():
										if (
										    k.endswith(""".self_attn.q_proj.weight"""	)
										    or k.endswith(""".self_attn.k_proj.weight"""	)
										    or k.endswith(""".self_attn.v_proj.weight"""	)
										):
															lowercase_					=      k[: -len(""".q_proj.weight"""	)]
															lowercase_					=      k[-len("""q_proj.weight"""	)]
															if k_pre not in capture_qkv_weight:
																				lowercase_					=      [None, None, None]
															lowercase_					=      v
															continue

										if (
										    k.endswith(""".self_attn.q_proj.bias"""	)
										    or k.endswith(""".self_attn.k_proj.bias"""	)
										    or k.endswith(""".self_attn.v_proj.bias"""	)
										):
															lowercase_					=      k[: -len(""".q_proj.bias"""	)]
															lowercase_					=      k[-len("""q_proj.bias"""	)]
															if k_pre not in capture_qkv_bias:
																				lowercase_					=      [None, None, None]
															lowercase_					=      v
															continue

										lowercase_					=      textenc_pattern.sub(lambda UpperCAmelCase__	: protected[re.escape(m.group(0	)	)]    ,				lowercase_	)
										lowercase_					=      v

					for k_pre, tensors in capture_qkv_weight.items():
										if None in tensors:
															raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""	)
										lowercase_					=      textenc_pattern.sub(lambda UpperCAmelCase__	: protected[re.escape(m.group(0	)	)]    ,				lowercase_	)
										lowercase_					=      torch.cat(lowercase_	)

					for k_pre, tensors in capture_qkv_bias.items():
										if None in tensors:
															raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""	)
										lowercase_					=      textenc_pattern.sub(lambda UpperCAmelCase__	: protected[re.escape(m.group(0	)	)]    ,				lowercase_	)
										lowercase_					=      torch.cat(lowercase_	)

					return new_state_dict


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					return text_enc_dict


if __name__ == "__main__":
							a       	=  argparse.ArgumentParser()

							parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
							parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
							parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
							parser.add_argument(
							    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
							)

							a       	=  parser.parse_args()

							assert args.model_path is not None, "Must provide a model path!"

							assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

							# Path for safetensors
							a       	=  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
							a       	=  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
							a       	=  osp.join(args.model_path, 'text_encoder', 'model.safetensors')

							# Load models from safetensors if it exists, if it doesn't pytorch
							if osp.exists(unet_path):
														a       	=  load_file(unet_path, device='cpu')
							else:
														a       	=  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
														a       	=  torch.load(unet_path, map_location='cpu')

							if osp.exists(vae_path):
														a       	=  load_file(vae_path, device='cpu')
							else:
														a       	=  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
														a       	=  torch.load(vae_path, map_location='cpu')

							if osp.exists(text_enc_path):
														a       	=  load_file(text_enc_path, device='cpu')
							else:
														a       	=  osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
														a       	=  torch.load(text_enc_path, map_location='cpu')

							# Convert the UNet model
							a       	=  convert_unet_state_dict(unet_state_dict)
							a       	=  {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							a       	=  convert_vae_state_dict(vae_state_dict)
							a       	=  {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

							# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
							a       	=  'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

							if is_vaa_model:
														# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
														a       	=  {'transformer.' + k: v for k, v in text_enc_dict.items()}
														a       	=  convert_text_enc_state_dict_vaa(text_enc_dict)
														a       	=  {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
							else:
														a       	=  convert_text_enc_state_dict(text_enc_dict)
														a       	=  {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							a       	=  {**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														a       	=  {k: v.half() for k, v in state_dict.items()}

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														a       	=  {'state_dict': state_dict}
														torch.save(state_dict, args.checkpoint_path)










'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

__lowerCAmelCase        =							logging.get_logger(__name__)


__lowerCAmelCase        =							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowerCAmelCase        =							{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowerCAmelCase        =							{"facebook/blenderbot-3B": 1_28}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			(
           list(range(ord('!'   )   ,       ord('~'   ) + 1   )   ) + list(range(ord('¡'   )   ,       ord('¬'   ) + 1   )   ) + list(range(ord('®'   )   ,       ord('ÿ'   ) + 1   )   )
       )
       a_						     =			bs[:]
       a_						     =			0
       for b in range(2**8   ):
              if b not in bs:
                     bs.append(lowercase_   )
                     cs.append(2**8 + n   )
                     n += 1
       a_						     =			[chr(lowercase_   ) for n in cs]
       return dict(zip(lowercase_   ,       lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			set()
       a_						     =			word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)   )
              a_						     =			char
       return pairs




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Dict      			=     VOCAB_FILES_NAMES
  _a  : Union[str, Any]      			=     PRETRAINED_VOCAB_FILES_MAP
  _a  : Optional[int]      			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : str      			=     ['''input_ids''', '''attention_mask''']







  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__="replace"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="<unk>"   ,	UpperCamelCase__="<pad>"   ,	UpperCamelCase__="<mask>"   ,	UpperCamelCase__=False   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else bos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else eos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else sep_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else cls_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else unk_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else mask_token

         super().__init__(
             errors=UpperCamelCase__   ,	bos_token=UpperCamelCase__   ,	eos_token=UpperCamelCase__   ,	unk_token=UpperCamelCase__   ,	sep_token=UpperCamelCase__   ,	cls_token=UpperCamelCase__   ,	pad_token=UpperCamelCase__   ,	mask_token=UpperCamelCase__   ,	add_prefix_space=UpperCamelCase__   ,	**UpperCamelCase__   ,	)

         with open(UpperCamelCase__   ,	encoding='utf-8'	) as vocab_handle:
                a_						     =			json.load(UpperCamelCase__	)
         a_						     =			{v: k for k, v in self.encoder.items()}
         a_						     =			errors  # how to handle errors in decoding
         a_						     =			bytes_to_unicode()
         a_						     =			{v: k for k, v in self.byte_encoder.items()}
         with open(UpperCamelCase__   ,	encoding='utf-8'	) as merges_handle:
                a_						     =			merges_handle.read().split('\n'	)[1:-1]
         a_						     =			[tuple(merge.split()	) for merge in bpe_merges]
         a_						     =			dict(zip(UpperCamelCase__   ,	range(len(UpperCamelCase__	)	)	)	)
         a_						     =			{}
         a_						     =			add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         a_						     =			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)







  @property
  # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def    _a       (			self	):






         """simple docstring"""


         return len(self.encoder	)







  def    _a       (			self	):






         """simple docstring"""


         return dict(self.encoder   ,	**self.added_tokens_encoder	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if token in self.cache:
                return self.cache[token]
         a_						     =			tuple(UpperCamelCase__	)
         a_						     =			get_pairs(UpperCamelCase__	)

         if not pairs:
                return token

         while True:
                a_						     =			min(UpperCamelCase__   ,	key=lambda UpperCamelCase__	: self.bpe_ranks.get(UpperCamelCase__   ,	float('inf'	)	)	)
                if bigram not in self.bpe_ranks:
                       break
                a_     ,	a_						     =			bigram
                a_						     =			[]
                a_						     =			0
                while i < len(UpperCamelCase__	):
                       try:
                              a_						     =			word.index(UpperCamelCase__   ,	UpperCamelCase__	)
                       except ValueError:
                              new_word.extend(word[i:]	)
                              break
                       else:
                              new_word.extend(word[i:j]	)
                              a_						     =			j

                       if word[i] == first and i < len(UpperCamelCase__	) - 1 and word[i + 1] == second:
                              new_word.append(first + second	)
                              i += 2
                       else:
                              new_word.append(word[i]	)
                              i += 1
                a_						     =			tuple(UpperCamelCase__	)
                a_						     =			new_word
                if len(UpperCamelCase__	) == 1:
                       break
                else:
                       a_						     =			get_pairs(UpperCamelCase__	)
         a_						     =			' '.join(UpperCamelCase__	)
         a_						     =			word
         return word







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for token in re.findall(self.pat   ,	UpperCamelCase__	):
                a_						     =			''.join(
                    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__	).split(' '	)	)
         return bpe_tokens







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.encoder.get(UpperCamelCase__   ,	self.encoder.get(self.unk_token	)	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.decoder.get(UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			''.join(UpperCamelCase__	)
         a_						     =			bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'   ,	errors=self.errors	)
         return text







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase__	):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                return
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as f:
                f.write(json.dumps(self.encoder   ,	indent=2   ,	sort_keys=UpperCamelCase__   ,	ensure_ascii=UpperCamelCase__	) + '\n'	)

         a_						     =			0
         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as writer:
                writer.write('#version: 0.2\n'	)
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,	key=lambda UpperCamelCase__	: kv[1]	):
                       if index != token_index:
                              logger.warning(
                                  f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                  ' Please check that the tokenizer is not corrupted!'	)
                              a_						     =			token_index
                       writer.write(' '.join(UpperCamelCase__	) + '\n'	)
                       index += 1

         return vocab_file, merge_file







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False	):






         """simple docstring"""


         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=UpperCamelCase__   ,	token_ids_a=UpperCamelCase__   ,	already_has_special_tokens=UpperCamelCase__	)

         if token_ids_a is None:
                return [1] + ([0] * len(UpperCamelCase__	)) + [1]
         return [1] + ([0] * len(UpperCamelCase__	)) + [1, 1] + ([0] * len(UpperCamelCase__	)) + [1]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=False   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			kwargs.pop('add_prefix_space'   ,	self.add_prefix_space	)
         if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__	) > 0 and not text[0].isspace()):
                a_						     =			' ' + text
         return (text, kwargs)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         return token_ids_a + [self.eos_token_id]







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for is_user, text in conversation.iter_texts():
                if is_user:
                       # We need to space prefix as it's being done within blenderbot
                       inputs.append(' ' + text	)
                else:
                       # Generated responses should contain them already.
                       inputs.append(UpperCamelCase__	)

         a_						     =			'  '.join(UpperCamelCase__	)
         a_						     =			self.encode(UpperCamelCase__	)
         if len(UpperCamelCase__	) > self.model_max_length:
                a_						     =			input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'	)
         return input_ids




